1. Technical Field
The present disclosure relates to vehicle detection and, more specifically, to a system and method for vehicle detection and tracking from a moving monocular camera.
2. Discussion of the Related Art
Vehicle safety and convenience features are an important and growing field of technology. Through the use of a video camera mounted on a moving vehicle, video signals may be analyzed using computer vision techniques to provide advanced driver assistance and potentially life saving support to the vehicle operator. For example, by analyzing video signals and/or other sensor data, the vehicle operator may be alerted to potential obstacles and hazards and in some cases, vehicle control may be automatically assisted.
When in motion, potential obstacles and hazards include objects that rise above the road plane such as pedestrians and other vehicles. Because other vehicles often present a particular hazard, the detection and tracking of other vehicles is an important part of computer vision-based safety and convenience features.
Thus, obstacle detection, for example vehicle detection, may receive a video signal from a video camera located within or upon the moving vehicle and recognize and track preceding vehicles from the driving scene. This information may then be utilized by one or more vehicle safety and convenience features, for example, adaptive cruise control, forward collision avoidance and lane change support.
A number of approaches have been developed to address vehicle tracking and detection. One category of obstacle detection methods uses motion analysis. In such methods, the motion of the video camera within the host vehicle is accounted for so that the motion vectors of other objects may be determined with reference to the stationary road. Objects determined to be in motion relative to the stationary road may be identified as potential obstacles and accordingly monitored.
Obstacle detection approaches using only motion analysis may be particularly sensitive to image noise and illumination changes. In addition, motion analysis alone may not be able to classify detected objects.
In another approach, a 3D polyhedral model is used to detect and track vehicles in a surveillance setup. A target vehicle is described in a 2D view sketch composed of edge segments specified by their length parameters. This approach requires the monitored zone to be stationary, thus the camera and the ego vehicle are not moving. When the camera together with the ego vehicle is moving, the technique of background subtraction and change detection is no longer able to separate a target vehicle from changing traffic scene.
In another known approach, distant vehicles are detected and identified by the detection of the vehicle's horizontal and vertical edges, aspect ratio check and correlation of the distant vehicle to predefined templates. If camera parameters are available, the distance of the detected vehicles from the host vehicle (i.e. ego-vehicle) can be estimated through the use of a perspective projection model. In some instances, the detection starts from feature selection and tracking. Tracked features are grouped into clusters corresponding roughly to different objects in the scene. Vehicles are identified and validated through edge finding. A simple vehicle classification scheme is used based on the aspect ratio to distinguish from among various categories of vehicles. In other approaches, a probabilistic model is used to model the strength of the edges around the vehicle boundary. In these approaches, vehicle detection is implemented by locating bounding boxes from edges and verifying the vehicle presence with the edge model. The detected vehicles may then be tracked with the use of and extended Kalman filter.
A second category of vehicle detection algorithms treats the detection as a two-class pattern classification problem involving a vehicle class and a non-vehicle class. Instead of using the empirical descriptions for the vehicle class, these algorithms use a classification function to tell if an image patch contains a vehicle or not. Through an offline training process, the best classification function with minimum classification error is learned from a number of vehicle and non-vehicle examples. The training process takes into account the variation of vehicle appearance within the training examples. Compared to the empirical vehicle model with edges, shapes and templates, the training and classification approach produces more reliable detection results.
In one approach, a vehicle detection algorithm is implemented in two steps: multi-scale hypothesis generation and appearance-based hypothesis verification. Appearance-based hypothesis verification verifies the hypothesis using wavelet feature extraction approach and Support Vector Machines (SVMs) as classifiers. In another approach, a Support Vector Tracking (SVT) method is introduced, which integrates the SVM classifier into an optical-flow based tracker. Instead of minimizing an intensity difference function between consecutive frames, SVT maximizes the SVM score such that the detection results have the highest confidence scores in the corresponding video frames.
In these approaches, the classifier response is computed over a neighborhood region around the vehicle position detected from the previous frame. The location with the highest response is considered the vehicle position in the current frame. The focus of such detection methods is to build an accurate and efficient vehicle detector (classifier). Tracking is considered a process of data association that links the detection results from individual frames to a temporal trajectory.
Accordingly, there is a present need for effective and efficient systems and methods for detecting and tracking preceding vehicles